Who consults chiropractors in Victoria, Australia?: Reasons for attending, general health and lifestyle habits of chiropractic patients
Bibliographic record
Abstract
BACKGROUND: COAST (Chiropractic Observational and Analysis STudy) reported the clinical practices of chiropractors. The aims of this study were to: 1) describe the chiropractic patient demographic and health characteristics; 2) describe patient-stated reasons for visiting a chiropractor; 3) describe chiropractic patient lifestyle characteristics; 4) compare, where possible, chiropractic patient characteristics to the general Australian population. METHODS: Fifty-two chiropractors in Victoria, Australia, provided information for up to 100 consecutive encounters. If patients attended more than once during the 100 encounters, only data from their first encounter were included in this study. Where possible patient characteristics were compared with the general Australian population. RESULTS: Data were collected from December 2010 to September 2012. Data were provided for 4464 encounters, representing 3287 unique individuals. The majority of chiropractic encounters were for musculoskeletal conditions or for wellness/maintenance. The majority of patient comorbidities were musculoskeletal, circulatory or endocrine/metabolic in nature. Eight hundred chiropractic patients (57 %, 95 % CI: 53-61) described their self-reported health as excellent or very good and 138 patients (10 %, 95 % CI: 8-12) as fair or poor. Seventy-one percent of adult male patients (18 years and older), and 53 % of adult female patients, were overweight or obese. Fourteen percent (n = 188, 95 % CI: 12-16) were current smokers and 27 % (n = 359, 95 % CI: 24-31) did not meet Australian alcohol consumption guidelines. Less than half of the chiropractic patients participated in vigorous exercise at least twice per week. Approximately 20 % ate one serving of vegetables or less each day, and approximately 50 % ate one serve of fruit or less each day. Compared to the general Australian population, chiropractic patients were less likely to smoke, less likely to be obese and more likely to describe their health in positive terms. However, many patients were less likely to meet alcohol consumption guidelines, drinking more than is recommended. CONCLUSIONS: In general, chiropractic patients had more positive health and lifestyle characteristics than the Australian population. However, there were a significant proportion of chiropractic patients who did not meet guideline recommendations about lifestyle habits and there is an opportunity for chiropractors to reinforce public health messages with their patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".